Workflow predictive analytics Engine

ABSTRACT

Systems, methods, and apparatus to generate and utilize predictive workflow analytics and inferencing are disclosed and described. An example apparatus includes processor(s) to at least: generate a prediction including a probability of a patient no-show to a scheduled appointment using an artificial intelligence engine including a patient no-show model to predict the probability of the patient no-show based on a combination of healthcare workflow data and non-healthcare information; synchronize the prediction and a schedule including the scheduled appointment; and generate an interactive dashboard including the synchronized prediction and the schedule and at least one of: a) a first option to adjust the schedule to replace the patient based on the probability of the patient no-show orb) a second option to adjust the schedule to move the patient to a different time based on the probability of the patient no-show.

CROSS-REFERENCE TO RELATED APPLICATION

This patent arises from U.S. patent application Ser. No. 16/456,656,which was filed on Jun. 28, 2019, and U.S. Provisional PatentApplication Ser. No. 62/770,548, which was filed on Nov. 21, 2018. U.S.patent application Ser. No. 16/456,656 is hereby incorporated herein byreference in its entirety. Priority to U.S. patent application Ser. No.16/456,656 is hereby claimed. U.S. Provisional Patent Application Ser.No. 62/770,548 is hereby incorporated herein by reference in itsentirety. Priority to U.S. Provisional Patent Application Ser. No.62/770,548 is hereby claimed.

FIELD OF THE DISCLOSURE

This disclosure relates generally to improved medical systems and, moreparticularly, to improved workflow predictive analytics engine systemsand associated methods.

BACKGROUND

The statements in this section merely provide background informationrelated to the disclosure and may not constitute prior art.

Healthcare environments, such as hospitals or clinics, includeinformation systems, such as hospital information systems (HIS),radiology information systems (RIS), clinical information systems (CIS),and cardiovascular information systems (CVIS), and storage systems, suchas picture archiving and communication systems (PACS), libraryinformation systems (LIS), and electronic medical records (EMR).Information stored can include patient medication orders, medicalhistories, imaging data, test results, diagnosis information, managementinformation, and/or scheduling information, for example. A wealth ofinformation is available, but the information can be siloed in variousseparate systems requiring separate access, search, and retrieval.Correlations between healthcare data remain elusive due to technologicallimitations on the associated systems.

Further, when data is brought together for display, the amount of datacan be overwhelming and confusing. Such data overload presentsdifficulties when trying to display, and competing priorities put apremium in available screen real estate. Existing solutions aredeficient in addressing these and other related concerns.

BRIEF DESCRIPTION

Systems, methods, and apparatus to generate and utilize predictiveworkflow analytics and inferencing are disclosed and described.

Certain examples provide a predictive workflow analytics apparatus. Theexample apparatus includes a memory including instructions; and at leastone processor to execute the instructions to at least: orchestrate acombination of non-healthcare information with healthcare workflow dataincluding patient information and a scheduled appointment for thepatient; generate a prediction including a probability of a patientno-show to the scheduled appointment using an artificial intelligenceengine in an inferencing mode, the artificial intelligence engineincluding a patient no-show model to predict the probability of thepatient no-show based on the combination of healthcare workflow data andnon-healthcare information; synchronize the prediction and a scheduleincluding the scheduled appointment for the patient; and generate aninteractive dashboard including the synchronized prediction and theschedule and at least one of: a) a first option to adjust the scheduleto replace the patient based on the probability of the patient no-show,b) a second option to adjust the schedule to move the patient to adifferent time based on the probability of the patient no-show.

Certain examples provide at least one computer-readable storage mediumincluding instructions which, when executed by at least one processor,cause the at least one processor to at least: orchestrate a combinationof non-healthcare information with healthcare workflow data includingpatient information and a scheduled appointment for the patient;generate a prediction including a probability of a patient no-show tothe scheduled appointment using an artificial intelligence engine in aninferencing mode, the artificial intelligence engine including a patientno-show model to predict the probability of the patient no-show based onthe combination of healthcare workflow data and non-healthcareinformation; synchronize the prediction and a schedule including thescheduled appointment for the patient; and generate an interactivedashboard including the synchronized prediction and the schedule and atleast one of: a) a first option to adjust the schedule to replace thepatient based on the probability of the patient no-show, b) a secondoption to adjust the schedule to move the patient to a different timebased on the probability of the patient no-show.

Certain examples provide a method to apply predictive analytics to drivea patient care pathway. The example method includes: orchestrating acombination of non-healthcare information with healthcare workflow dataincluding patient information and a scheduled appointment for thepatient; generating a prediction including a probability of a patientno-show to the scheduled appointment using an artificial intelligenceengine in an inferencing mode, the artificial intelligence engineincluding a patient no-show model to predict the probability of thepatient no-show based on the combination of healthcare workflow data andnon-healthcare information; synchronizing the prediction and a scheduleincluding the scheduled appointment for the patient; and generating aninteractive dashboard including the synchronized prediction and theschedule and at least one of: a) a first option to adjust the scheduleto replace the patient based on the probability of the patient no-show,b) a second option to adjust the schedule to move the patient to adifferent time based on the probability of the patient no-show.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example predictive analytics inferencingarchitecture.

FIG. 2 illustrates a more detailed view of an implementation of theexample architecture of FIG. 1.

FIG. 3 depicts an example implementation of the inferencing engine ofFIGS. 1-2.

FIG. 4 depicts another example predictive analytics inferencingarchitecture.

FIG. 5 illustrates a more detailed view of an implementation of theexample architecture of FIG. 4.

FIG. 6 shows example training and testing processes and associatedpipelines for the AI model of FIGS. 1-5.

FIGS. 7-8 show example flow charts integrating artificialintelligence-driven prediction and modeling into an example patient carepathway.

FIGS. 9-14 depict example interfaces generated by the example systemsand methods of FIGS. 1-8.

FIG. 15 is a block diagram of an example processor platform capable ofexecuting instructions to implement the example systems and methodsdisclosed and described herein.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings that form a part hereof, and in which is shown byway of illustration specific examples that may be practiced. Theseexamples are described in sufficient detail to enable one skilled in theart to practice the subject matter, and it is to be understood thatother examples may be utilized and that logical, mechanical, electricaland other changes may be made without departing from the scope of thesubject matter of this disclosure. The following detailed descriptionis, therefore, provided to describe an exemplary implementation and notto be taken as limiting on the scope of the subject matter described inthis disclosure. Certain features from different aspects of thefollowing description may be combined to form yet new aspects of thesubject matter discussed below.

When introducing elements of various embodiments of the presentdisclosure, the articles “a,” “an,” and “the” are intended to mean thatthere are one or more of the elements. The terms “first,” “second,” andthe like, do not denote any order, quantity, or importance, but ratherare used to distinguish one element from another. The terms“comprising,” “including,” and “having” are intended to be inclusive andmean that there may be additional elements other than the listedelements. As the terms “connected to,” “coupled to,” etc. are usedherein, one object (e.g., a material, element, structure, member, etc.)can be connected to or coupled to another object regardless of whetherthe one object is directly connected or coupled to the other object orwhether there are one or more intervening objects between the one objectand the other object.

As used herein, the terms “system,” “unit,” “module,” “engine,” etc.,may include a hardware and/or software system that operates to performone or more functions. For example, a module, unit, or system mayinclude a computer processor, controller, and/or other logic-baseddevice that performs operations based on instructions stored on atangible and non-transitory computer readable storage medium, such as acomputer memory. Alternatively, a module, unit, engine, or system mayinclude a hard-wired device that performs operations based on hard-wiredlogic of the device. Various modules, units, engines, and/or systemsshown in the attached figures may represent the hardware that operatesbased on software or hardwired instructions, the software that directshardware to perform the operations, or a combination thereof.

In addition, it should be understood that references to “one embodiment”or “an embodiment” of the present disclosure are not intended to beinterpreted as excluding the existence of additional embodiments thatalso incorporate the recited features.

Aspects disclosed and described herein provide systems and associatedmethods to provide predictive analytics including customer-drivenworkflow predictions and corresponding responsive actions. For example,predictive analytics disclosed and described herein can be used to avoidbreaches in service level agreement (SLA) with respect to reporting,etc., by providing a real-time reporting worklist at a point of decision(e.g., in a radiology information system (RIS), etc.) along with aprobability of breach. In another example, patient no-shows can beprevented by identifying examinations having a high probability ofno-show and triggering a corrective action such as a reminder, anoverbooking analysis, a ride sharing assist, etc., as disclosed anddescribed herein. In another example, patient waiting time can bepredicted to improve patient experience and revenue opportunity bycomputing and announcing an estimated waiting time as disclosed anddescribed herein. In another example, workload and capacity can bemanaged by planning strategically on a machine and reporting resourcesneeded for each service as disclosed and described herein.

For example, patient no-shows for radiology appointments can bepredicted using historical patterns and artificial intelligenceprocessing of patient information (e.g., age, gender, history, etc.),appointment age (e.g., how long since the appointment was made, etc.),date/time of appointment, weather forecast, other historical patterndata, etc. Certain examples leverage artificial intelligence (AI) suchas a random forest, artificial neural network (such as a convolutionalneural network (CNN), etc.), etc., to provide an integrated predictionand corrective action framework to address likely patient no-show.Patient no shows are costly (e.g., ˜$1M loss in yearly opportunity formagnetic resonance imaging exams at a 4% patient no-show rate). Amachine learning algorithm and associated model can factor in elementssuch as weather forecast, location, time, traffic, etc., to predictlikely patient no-shows, and a reduced in no-shows increasesresponsiveness to patient health needs, increased productivity in ahealthcare environment, increased revenue, etc., through algorithm-basedreconfirmation/replacement strategies, for example.

FIG. 1 illustrates an example predictive analytics inferencingarchitecture 100. The example apparatus 100 includes and/or interactswith one or more workflow information systems 110, such as an electronicmedical record (EMR) system, radiology information system (RIS), picturearchiving and communication system (PACS), etc. The informationsystem(s) 110 provide healthcare workflow data 115 to a data store 120,such as an ElastiCube, other data cube, other data store, etc. Theworkflow data 115 can related to a schedule or workflow of activitiesinvolving patients, resources, personnel, etc., for a healthcarefacility, for example. The workflow data 115 can be mined using extract,transform, and load (ETL) operations to provide the data 115 to thestorage 120, for example. The data storage 120 provides the data to apredictive analytics dashboard 130 as well as a data access layer 140.The dashboard 130 can display prediction(s) from the data 115, forexample. The data access layer 140 receives data from the data store 120(e.g., via a Representational State Transfer (REST) get request, etc.)and combines the data with additional information such as weatherforecast information 150 (traffic information, non-healthcare eventinformation, etc.). The data access layer 140 combines the healthcaredata 115, such as appointment data, patient data, hospital resourcedata, etc., with weather forecast information 150 (e.g., looking at a5-day window around the time of the appointment, etc.) and/or otherinformation such as location, traffic, etc., to form combined, enrichedhealthcare workflow data, and provides the combined, enrichedinformation (e.g., via a REST post operation, etc.) to a machinelearning inferencing engine 160, which includes one or more AI models165 to process the information and generate a prediction, for example.Results are provided (e.g., via a REST result operation, etc.) back tothe data access layer 140 to be conveyed to the data store 120 as wellas to the information system(s) 110 as one or more integrated workflowpredictions 170.

Thus, data can be aggregated and processed by one more machine learningalgorithms implemented using models 165 (e.g., random forest, CNN, etc.)to provide predictive output 170 to the information system(s) 110. Thealgorithm can change based on a goal of the analysis, degree ofprobability estimation, accuracy, priority, etc. The example dashboard130 can provide both predictive and retrospective visualization(s) ofinformation, such as prediction of one or more patient no-shows, etc. Incertain examples, a confidence interval can be provided with thepredictive estimate. For example, using the prediction of a patientno-show and an associated confidence interval or score (e.g., 90%, 50%,30%, etc.), the system 110 can decide whether it wants to make anadjustment or change to that patient's appointment (e.g., a reminder, aconfirmation, a replacement or substitution of that time slot andassociated resource(s), etc.). The confidence interval can be aconfidence in the prediction based on available information, and/or theconfidence interval can be an indication of confidence that the patientwill show up for his/her scheduled appointment, for example.

For example, the prediction can analyze the schedule three days inadvance to identify patient(s) associated with a low confidence interval(e.g., <50%), then follow-up with them to confirm whether or not theywill be showing up. While rescheduling on the same day is difficult, theschedule can be adjusted up to one day in advance to accommodate apatient who will not or is not likely to attend his/her scheduledappointment. In certain examples, a more urgent patient can be scheduledin place of a patient with a low likelihood of attendance. If a patientis not likely to attend, degradable materials such as nuclear medicineisotopes can be saved, postponed, used instead for another patient,etc., rather than going to waste because the half-life does not allowstorage for delayed use.

In certain examples, the output 170 can include a worklist with anindication of confidence/likelihood in attendance/no show, etc. Incertain examples, the worklist is generated for follow-up, and patientson the list are prioritized or ranked based on their priority, theirlikelihood of no show, and available capacity when the list is too longto follow up with everyone.

In certain examples, the worklist can be processed by looking forpatients with a same or similar procedure scheduled in the next month tosee if a slot can be filled with someone else if the patient currentlyin that slot does not make his/her appointment. In certain examples,patient address can be compared to clinic location and combined withtraffic information to priority patient(s) who can more easily make itto the hospital to fill a time slot.

In certain examples, the data store 120 transforms the data 115 beforeproviding the data to the data access layer 140 and inferencing engine160. For example, the data store 120 can transform a format of the data115, can organize/arrange the data 115, etc. Thus, data 115 can betransformed from the RIS to generate a priority list, for example. Themodel 165 provides output to the data store 120 to be used by thedashboard(s) 130 to present predictive results. The data store 120 cantransform the output from the model(s) 165 of the inferencing engine 160to form a predictive dashboard display 130, for example. Thus, datamodeled in the data store 120 (e.g., cleaned,standardized/normalized/transformed, and prepared for output, etc.) canbe used to train model(s) 165 and generate prediction(s), for example.The data store 120 can be configured to select a particular subset ofthe data 115, rather than all the data 115, from the informationsystem(s) 110 that matches certain constraint(s), criterion(-ia), etc.,and the data store 120 can organize that data in a certain way for thedashboard(s) 130, model(s) 165, etc.

The model(s) 165 are trained using the prepared data from the data store120 as further combined with other information such as weather 150,traffic, etc., via the data access layer 140. The data and constraintstrain, test, and transform the model(s) 165 into particular algorithmscustomized for the specific data set(s) 115 and observed patientpattern(s) for the particular healthcare environment's system(s) 110.Thus, the model(s) 165 become a customized algorithm or set ofalgorithms that function for a particular environment, scenario, set ofresources, patient population, etc.

In certain examples, the data access layer 140 can send results to therelevant system(s) 110, such as a RIS, PACS, EMR, etc., and appointmentscan be directly flagged in the RIS scheduling system with a highprobability of no-show. Action can be taken to confirm thoseappointments, cancel or reschedule those appointments, fill in emptytime slots with other available patients, etc.

FIG. 2 illustrates a more detailed view of an implementation of theexample architecture 100. In the example of FIG. 2, the architecture 100is implemented as a virtual machine or appliance running at a healthcarefacility (e.g., a hospital, clinic, doctor's office, etc.). In theexample implementation of FIG. 2, the data store 120 is divided into aMS data cube 120 and a result cube 125, and the data access layer 140includes a data access service 142 and a data access controller 144. Thedata access layer 140 provides a result that is saved in a result file210, which is provided to the result cube 125. The scheduled build ofpredictive results from the result file 210 can be used to drive thedashboard 130 interface display(s) and associated action(s).

As shown in the example of FIG. 2, an event at a workflow informationsystem 110 triggers (e.g., based on an appointment or schedulingrequest, daily schedule generation, etc.) exchange of data andprocessing of event data, patient data, and other data (e.g., non-healthdata such as weather, traffic, resources, etc.) to generate aninteractive dashboard display and schedule modification. The data cube120 merges data from multiple sources and enables components of thesystem 100 to manipulate and query the data as if it was oneconsolidated data set. Using the cube 120, data from one or more sources110 at one or more locations can based “mashed” together to representdata in fields in which a value in one field has a corresponding valuein another field to enable data in a field to be processed with respectto data in any other field. By allowing data to be analyzed in thecontext of other data from the same or disparate data source, the cube120 enables powerful query and analysis of large amounts of data fromdisparate data source(s) to be processed by the data access layer 140 inreal time (or substantially real time given a data retrieval, storage,and/or processing latency, etc.).

In certain examples, the data 115 can be provided to the cube 120 viaextract, transform, and load (ETL) operation(s). Using ETL, data 115 canbe copied from a source in one context to a destination in anothercontext. Thus, the ETL operation(s) process data retrieved from one ormore source(s) 110, cleanse the data to remedy deficiency,inconsistency, etc., from an expected format and/or context, andtransform the data into a format/context on which the data access layer140 can act. In certain examples, ETL operation(s) on the data 115 formthe data 115 into a comma separated value (CSV) file and/or otherspreadsheet, data file, etc., for retrieval and processing by the dataaccess layer 140.

In certain examples, the data access layer 140 creates a layer ofabstraction between the data cube 120 and the inference engine 160. Theabstraction of the data access layer 140 allows different logical modelsto be used with respect to data in the data cube 120 and processing viathe inference engine 160 and its model(s) 165, for example. In certainexamples, the data access layer 140 can include business logic to tailorqueries of data via the data cube 120 and provide an incoming query ofthe data cube 120 (e.g., data gathered from the cube 120 via a REST getquery, etc.) and an outgoing result for the result cube 125. As shown inthe example of FIG. 2, the data access layer 140 includes a data accessservice 142 and a data access controller 144. The data access controller144 regulates the data access service 142 to get and combine data,process the data, trigger inferencing by the inferencing engine 160,etc. The data access controller 144 can help ensure quality and quantityof data retrieved by the data access service 142 and can help ensureauthentication and authorization to retrieve, combine, and process data,for example. For example, the data access controller 144 can control(e.g., via a hypertext transfer protocol (HTTP) request, etc.) the dataaccess service 142 to gather patient and schedule data 115 as well asweather information 150 for a particular time/location to form anexecution request for the inference engine 160.

Thus, the data access layer 140 receives data from the data store 120(e.g., via a REST get request, etc.) and combines the data withadditional information such as weather forecast information 150 (trafficinformation, non-healthcare event information, etc.). The data accesslayer 140 combines the healthcare data 115, such as appointment data,patient data, hospital resource data, etc., with weather forecastinformation 150 (e.g., looking at a 5-day window around the time of theappointment, etc.) and/or other information such as location, traffic,etc., and provides the combined information (e.g., via a REST postoperation, etc.) to the machine learning inferencing engine 160, whichincludes one or more AI models 165 to process the information andgenerate a prediction, for example. The inference engine 160 trains anddeploys AI model(s) 165 such as machine learning models (e.g., neuralnetworks, etc.), etc., to process incoming data and determine a likelyoutcome such as a no-show prediction, etc. The model(s) 165 can betrained, for example, on prior, verified data indicating that certainpatient conditions, weather, time/location, etc., result in a patientno-show for an appointment, for example. Results are provided (e.g., viaa REST result operation, etc.) back to the data access layer 140 to beconveyed as an output, such as a CVS file 210, etc., to the result datacube 125 as well as to the information system(s) 110 as one or moreintegrated workflow predictions 170, for example.

FIG. 3 depicts an example implementation of the inferencing engine 160of FIGS. 1-2. As shown in the example of FIG. 3, the inferencing engine160 can be implemented as a container or virtual machine including aplurality of elements or actions 302-314. For example, the inferencingengine of FIG. 3 includes an HTTP request receiver 302 to perform inputvalidation, request processing, etc. The receiver 302 provides theprocessed data to a context creator 304, which creates apatient/schedule context using the input data. For example, context suchas reason for exam, patient condition, location, time, etc., can beassociated with the data. The data with context is then provided to apreprocessing algorithm 306, which prepares the data for processing bythe AI model(s) 165 to generate a prediction (e.g., a no-showprediction, an SLA breach prediction, a wait time prediction, a workloadprediction, etc.). The prediction is then output to an algorithmpostprocessor 310 to take the model 165 result(s) and formulate theresult(s) for use in display, records, schedule adjustment,communication, other output, etc. The post-processed result(s) areprovided an output contextualizer 312 to provide context (e.g., patientcontext, schedule context, etc.) to the output. The contextualizedoutput is then provided to a response generator 314 to create a response(e.g., an HTTP response, etc.) to be send to the data access controllerservice 144 of the data access layer 140, for example.

Thus, the inferencing engine 160 is a framework component that providesconnectivity and expansibility to accommodate one or more algorithmmodels 165, pre- and/or post-processing, and scalability to scale upalgorithm(s) to support a workflow across one or more hospitaldepartments, teams, etc. The engine 160 can scale predictive analyticsin the model(s) 165 for a number of sources, number of recipients,intended audience/environment, etc. In certain examples, a variety ofmodels 165 can be plugged in to the engine 160 depending on targetgoal/objective, patient population, healthcare environment, etc., themodel(s) 165 are incorporated into the engine 160 transparent to theuser and/or healthcare system 110. The engine 160 provides a frameworkto accept the algorithm model 165 and adapt that model 165 to a realworld system 110, for example.

For example, the model 165 is unable to connect to other parts of thesystem 110, and the engine 160 connects the model 165 to the system 100,allows it to be changed, enables it to be used, etc. The framework ofthe engine 160 anchors the model 165 and establishes connections withother parts of the system 100. For example, data from which theprediction is made comes from the database/cubes 120, forwarded via thedata management service of the access layer 140, and the inferencingengine 160 exposes an HTTP endpoint, for example, to receive the dataand process the data to help ensure quality, format, etc. Thepre-processed data is then forwarded to the model 165. Code executed bythe engine 160 before the model 165 and after the model 165 preprocessesdata going into the model 165 and post-processes data coming out of themodel 165 to be used by the system 100 after the model 165.

In certain examples, the model 165 is generated as a random forestmodel. Random forests or random decision forests are an ensemblelearning method for classification, regression and other tasks thatoperate by constructing a multitude of decision trees at training timeand outputting a class that is a mode of included classes(classification) or a mean prediction (regression) of the individualtrees, for example. Random decision forests correct for decision trees'habit of overfitting to their training set, for example. That is,decision tree structures can be used in machine learning, but, when thetree grows deep, the tree can learn irregular patterns, resulting in lowbias but high variance as the decision tree overfits its training dataset. Random forests average multiple deep decision trees, trained ondifferent parts of the same training set, to reduce variance. Thereduction in variance can come at the expense of a small increase in thebias and some loss of interpretability, but, generally, greatly boostsperformance in the final model. Random forests can be used to rankimportance of variables in a regression or classification problem, suchas a likelihood or probability of patient no-shows, in a natural way.

In certain examples, random forest predictors can lead to adissimilarity measure among observations. A random forest dissimilaritymeasure can also be defined between unlabeled data. A random forestdissimilarity can be used to process mixed variable types because it isinvariant to monotonic transformations of the input variables and isrobust to outlying observations. The random forest dissimilarityaccommodates a large number of semi-continuous variables due to itsintrinsic variable selection. For example, a random forest dissimilaritycan be used to weigh a contribution of each available variable accordingto how dependent the variable is on other variables. The random forestdissimilarity can be used to identify a set of patient(s) among a groupof scheduled patients who are likely to not show for their scheduledappointment based on past history, weather, traffic, etc.

Machine learning techniques, whether random forests, deep learningnetworks, and/or other experiential/observational learning system, canbe used to locate an object in an image, understand speech and convertspeech into text, establish correlations and/or prediction of an eventsuch as a patient no-show, improve the relevance of search engineresults, etc., for example. Deep learning is a subset of machinelearning that uses a set of algorithms to model high-level abstractionsin data using a deep graph with multiple processing layers includinglinear and non-linear transformations. While many machine learningsystems are seeded with initial features and/or network weights to bemodified through learning and updating of the machine learning network,a deep learning network trains itself to identify “good” features foranalysis. Using a multilayered architecture, machines employing deeplearning techniques can process raw data better than machines usingconventional machine learning techniques. Examining data for groups ofhighly correlated values or distinctive themes is facilitated usingdifferent layers of evaluation or abstraction.

Deep learning in a neural network environment includes numerousinterconnected nodes referred to as neurons. Input neurons, activatedfrom an outside source, activate other neurons based on connections tothose other neurons which are governed by the machine parameters. Aneural network behaves in a certain manner based on its own parameters.Learning refines the machine parameters, and, by extension, theconnections between neurons in the network, such that the neural networkbehaves in a desired manner.

Deep learning that utilizes a convolutional neural network segments datausing convolutional filters to locate and identify learned, observablefeatures in the data. Each filter or layer of the CNN architecturetransforms the input data to increase the selectivity and invariance ofthe data. This abstraction of the data allows the machine to focus onthe features in the data it is attempting to classify and ignoreirrelevant background information.

Deep learning operates on the understanding that many datasets includehigh level features which include low level features. While examining animage, for example, rather than looking for an object, it is moreefficient to look for edges which form motifs which form parts, whichform the object being sought. These hierarchies of features can be foundin many different forms of data such as speech and text, etc.

Learned observable features include objects and quantifiableregularities learned by the machine during supervised learning. Amachine provided with a large set of well classified data is betterequipped to distinguish and extract the features pertinent to successfulclassification of new data.

A deep learning machine that utilizes transfer learning may properlyconnect data features to certain classifications affirmed by a humanexpert. Conversely, the same machine can, when informed of an incorrectclassification by a human expert, update the parameters forclassification. Settings and/or other configuration information, forexample, can be guided by learned use of settings and/or otherconfiguration information, and, as a system is used more (e.g.,repeatedly and/or by multiple users), a number of variations and/orother possibilities for settings and/or other configuration informationcan be reduced for a given situation.

An example deep learning neural network can be trained on a set ofexpert classified data, for example. This set of data builds the firstparameters for the neural network, and this would be the stage ofsupervised learning. During the stage of supervised learning, the neuralnetwork can be tested whether the desired behavior has been achieved.

Once a desired neural network behavior has been achieved (e.g., amachine has been trained to operate according to a specified threshold,etc.), the machine can be deployed for use (e.g., testing the machinewith “real” data, etc.). During operation, neural networkclassifications can be confirmed or denied (e.g., by an expert user,expert system, reference database, etc.) to continue to improve neuralnetwork behavior. The example neural network is then in a state oftransfer learning, as parameters for classification that determineneural network behavior are updated based on ongoing interactions. Incertain examples, the neural network can provide direct feedback toanother process. In certain examples, the neural network outputs datathat is buffered (e.g., via the cloud, etc.) and validated before it isprovided to another process.

FIG. 4 depicts an alternate example predictive analytics inferencingarchitecture 400 to identify/predict patient no-shows, similar to butdifferent from the example architecture 100 of FIG. 1. While the exampleof FIG. 4 is directed to predicting and/or otherwise identifying patientno-shows (e.g., for scheduled exams, procedures, other appointments,etc.), the example architecture 400 can be leveraged for otheranalytics, modeling, and/or reactive prediction such as patient waittime, diagnosis, imaging insights, predictive reporting, etc. Theexample architecture 400 includes a query or data extract 1 (e.g., aJava Database Connectivity (JDBC) query, CSV extract, etc.) from a datasource 410, such as an EMR, HIS, RIS, etc., to be stored in a datawarehouse (DW) 420. An application programming interface (API) 425, suchas a REST API, etc., can be used to provide data 2 from the datawarehouse 420 to an AI engine 430 (e.g., a no-show AI engine 430), whichincludes one or more AI model constructs 435 (e.g., a no-show model,etc.). In certain examples, the API 425 can be implemented as a FastHealthcare Interoperability Resources (FHIR) API 425 defining FHIRresources as a set of operations or interactions with respect toresources in which resources are managed by type. FHIR allows data fromthe data warehouse 420 to be exposed for training of the model 435,inferencing using the model 435, etc. The data warehouse 420 combinesdata from a plurality of sources including the information system 410and other clinical and/or non-clinical data sources such as one or moresocial, weather, traffic, holiday/calendar, and/or other data stream,dispatch, query, etc. For example, a cloud-based data source 440 canprovide weather forecast, holiday calendar, and/or other information tobe combined with patient health data, schedule information, capacity,resources, etc., at the AI engine 430. Data is provided to the AI engine430 and used to generate a predictive output (e.g., a prediction ofpatient no-show) with the model 435, for example. The FHIR API 425enables output of the engine 430 to be integrated with otherhospital/clinical systems, for example.

Information from the data warehouse 420 and the AI engine 430 can beprovided 3 to a data store 450, such as a Sisense ElastiCube analyticsdatabase and/or other analytics/relational data store. The data store450 organizes data for analysis and querying, such as to generate 4 adashboard 460 for end user interaction. For example, a predictive examcount, no-show probability by time of day, appointment type, patienttype, etc., can be displayed for viewing, interaction, etc., via thedashboard 460. The dashboard 460 can also be a retrospective dashboardrecapping a number of exams and a corresponding number of patientno-shows, a breakdown of patient no-show by age and/or otherdemographic, etc. Interaction with the dashboard 460 can driveimprovement of the model 435, other adjustment of the engine 430, actionat a clinical system 410 (e.g., a change in schedule, duration, resourceallocation, etc.), etc.

FIG. 5 depicts the example architecture 400 in operation to predictpatient no-shows and generate a dashboard display. As shown in theexample of FIG. 5, a query 505 can be scheduled of the data source 410,such as a RIS, etc., to provide an update, download, dispatch, etc., tothe data warehouse 420 (e.g., a 2:00 AM read-only data dump from the RSI410 to the data warehouse 420, etc.). The data warehouse 420 organizesthe data according to one or more structured query language (SQL)tables, FHIR documents, etc., for further query, retrieval, analysis,processing, association, etc. Information 515 (e.g., read only memoryexchange, etc.) can be exchanged between the data warehouse 420 and anapplied intelligence engine 525 including the data cube(s) 450, one ormore dashboard 460, etc. The data cube 450 can be used to combine,synchronize, and orchestrate information for predictive output,dashboard 460 generation, dashboard 460 display, etc. Additionally,information 535 regarding no-shows, etc., can provided from a data store545 of predictions, patient history, site configuration information,etc. Schedule data and no-show data can be synchronized using asynchronizer 555 to provide information to the applied intelligenceengine 525 to be organized, correlated, and stored in the data cube 450,used to generate the dashboard(s) 460, etc. The synchronizer 555combines a retrospective views of data with a prediction to generatecontent for the dashboard 460, etc.

As shown in the example of FIG. 5, an orchestration service 565 servesas an intermediary between the data store 545, the externalweather/holiday/traffic data source 440, and the engine 430 (e.g., ininferencing, rather than training, mode) to correlate weather, holiday,traffic, and/or other information with patient no-show prediction, siteinformation, etc., to drive prediction of patient no-shows, etc. Theorchestration service 565 can leverage the FHIR API 425 to get and/orpost appointments for a particular location, patients involved in theappointments, etc. Output can be provided via the API 425 in abi-lateral link with the RIS 410 and/or other scheduling system. Assuch, when an available appointment slot is retrieved, provided,displayed, suggested, etc., a probability of patient no-show associatedwith that appointment slot is also determined and provided. A scheduleof appointments can be filled and/or otherwise adjusted according to aprobability of patient no-show in one or more scheduled appointmentslots, for example.

FIGS. 6A-6B show example pipelines 600, 650 for training 600 andinferencing 650 of the AI engine 430 and its model 435. The exampletraining pipeline 600 is used for initial training and re-training aftera certain time period has passed, a certain level of feedback has beenreceived, a certain error threshold in predictive no-showpercentage/rate/etc. has been exceed, etc. The example training pipeline600 processes a historical data set from a source system 410 (e.g., RIS,EMR, HIS, PACS, CVIS, etc.). The historical data is loaded into the datawarehouse 420 using one or more data ingestion scripts 610 that canextract, transform, format, organize, query, etc., the data from thesource system 410 for storage, correlation, other analysis, etc., in thedata warehouse 420. The FHIR API 425 can be used to extract historicaltraining data from the data warehouse 420 to be processed by the no-showorchestration service 565 to provide input to the model 435 via atraining pipeline 620. Data in the training pipeline 620 is provided asinput to train the model 435. One or more model training metrics 630 canbe generated analyzed to determine when the model 435 has been trainedand is ready for deployment, use, etc., versus adjusting model 435weights, parameters, etc., to continue the training 600.

Once training is complete, the model 435 can be deployed and/orotherwise activated in an inferencing mode 650. In certain examples,inferencing 650 can be performed daily on a daily update of data fromthe source system 410. The source system 410 provides a daily refresh tothe data ingestion script(s) 610, which store the information in thedata warehouse 420. The FHIOR API 425 provides information (e.g., a nextfive days of appointments, a next day's appointments, a next week'sappointments, etc.) to the no-show orchestration service 565 to preparethe information for submission via an inferencing pipeline 660 for inputto the AI model 435. The model 435 provides a prediction 670 (e.g., aprediction of patient no-show, etc.), which can be evaluated foraccuracy. An accuracy of prediction 670 can be evaluated over time andcan trigger retraining 680 if the accuracy of prediction 670 is lessthan a threshold, outside a range, and/or otherwise fails to satisfy aretraining criterion, for example.

FIG. 7 shows an example flow chart integrating AI-driven prediction andmodeling into an example patient care pathway 700. At block 710, arequest is generated. Machine learning-based prescription support 712can be provided with an exam request, scheduling request, prescriptionrequest, etc., as determined (e.g., using an AI model, etc.) to beappropriate 714 to a given context (e.g., user context, applicationcontext, healthcare context, etc.). Appropriateness 714 can be evaluatedusing an AI model to correlate patient characteristics, symptoms, etc.,to prescription information to treat the symptoms for the particularpatient composition, for example.

At block 720, scheduling is performed. For example, a predictive no-showalgorithm model 165 can be leveraged by a smart scheduler 724 to providesmart scheduling and reduce missed appointments, underutilizedresources, delayed patient care, etc. For example, a predictedlikelihood of patient no-shows at a location for a time period can beused by the smart scheduler 724 to dynamically adjust or generate aschedule for the time period accounting for likely/unlikely no-shows.Such a schedule can include one or more patients pre-scheduled to fillin for one or more patients missing appointments, one or more patientson standby to fill missing appointment(s) on short notice (e.g., basedon likelihood of availability, distance from location, urgency, etc.),etc. At block 720, scheduling can also leverage imaging insights 726such as a convolutional neural network-based image analysis, etc., toidentify one or more objects of interest in an image and/or otherwiseperform computer-aided diagnosis of the image data to uncover a likelydiagnosis, further aspects to evaluate, etc., which can be automaticallyscheduled (or suggested to be scheduled) for the patient. Imaginginsights 726 can be combined with asset utilization and case loadmanagement 728 to schedule patients, staff, and equipment to efficientlyaccommodate patient, staff, and equipment needs/requirements in anallotted period of time. For example, another AI model can processpatients and exams/tests/procedures/etc. to be scheduled with respect toavailable personnel and equipment resources to provide thoseexams/tests/procedures/etc., combined with a likelihood of patientno-show and/or other analysis, and provide input to scheduleappointments for the time period for the location.

At block 730, data acquisition is conducted. For example, acquisitioncan leverage a predictive wait time algorithm 732, imaging optimization736 (e.g., MR optimization, etc.), AI-based computer-aided diagnosis(CAD) 738, etc., to acquire information regarding the patient. Forexample, the predictive wait time algorithm 732 can provide anindication of how long a patient will wait to be seen, how long anexam/test/procedure will take to be conducted, etc. Such predicted waittime information can be leveraged to provide an improved patientexperience 734, for example. Image acquisition settings, etc., can beoptimized 736 to improve image acquisition time, image quality,diagnostic analysis, etc., and an AI model can be applied to theresulting image data (e.g., a CNN, other deep learning neural networkmodel, etc.) to predict and/or otherwise provide a diagnosis from theavailable image and/or other data for the patient, for example.

At block 740, reading and reporting can be provided using a smarthanging protocol 742 to arrange images, test results, exam data, patienthistory, and/or other information for clinician review. Predictivereporting 744 can provide an indication of a potential SLA breach,auto-populate findings for review and/or further processing, etc., forimproved performance 746 in reporting, diagnosis, treatment, propagationof information for further processing, etc.

FIG. 8 provides an example illustration of the scheduling (e.g., block720) of patient care including prediction of and reaction to patientno-shows. At block 810, a patient care workflow is identified (e.g.,provided in an exam request/reason for exam, extracted from a patientrecord, identified in a departmental schedule, etc.). For example, aschedule of one or more patients to be seen by one or more healthcarepractitioners at a hospital can be retrieved from a hospital informationsystem 110. At block 820, data related to the identified patient and/orpatient care workflow is mined for predictive analytics. For example,data 115 for the patient(s) on the schedule, healthcare practitioner(s)involved in the schedule, resource(s) involved in the schedule, etc.,can be extracted from one or more systems 110 such as a HIS, RIS, CIS,CVIS, PACS, LIS, EMR, etc., and mined for predictive analysis. The datacube 120 can format the data 115, combine the data 115, and/or otherwisetransform the data 115 to be processed by the inferencing engine 160,for example.

At block 830, mined data is combined with non-healthcare data such asappointment data, weather data, traffic data, resource information, etc.For example, the mined healthcare data 115 is enriched with weather data150, traffic information relative to patient, provider, and/or otherhealthcare location, etc. Thus, the healthcare data can be enriched withnon-healthcare data providing context, environment, conflicting scheduleconstraints, etc., that can affect predictive analysis of a future eventsuch as a patient no-show for a scheduled appointment, etc.

At block 840, the combined information is provided to the machinelearning inference engine 160 to generate a prediction regarding anoutcome associated with the information (e.g., a likelihood orprobability of a patient not showing up for an appointment, etc.). Forexample, a random forest model 165 can be used to represent scheduledata, workflow data, patient information, weather and/or trafficprojection(s), etc., using a plurality of decision trees. The decisiontrees can be constructed at training time using known or “ground truth”verified data. Upon deployment in the inference engine 160, the model(s)165 can output a mode regression and/or mean classification of thedecision trees representing a probability of patient no-show, forexample.

At block 850, a confidence score and/or interval associated with theprediction is computed. For example, the model 165 may output a yes orno answer and/or a percentage probability that a patient under reviewwill not attend his/her appointment (a no show). The confidence intervalassociated with the model determination can be formed, for example, bydetermining a mean probability of patient no show and a standarddeviation from the mean over a plurality of determinations (e.g., takinga square root of squared differences in range of availabledeterminations, etc.) and calculating a margin of error using the mean,standard deviation, a desire confidence level (e.g., 90%, 95%, 99%,etc.). The margin of error can be subtracted from the mean and added tothe mean to determine your confidence interval around the calculatedvalue from the model 165, for example.

At block 860, an output is generated. For example, a worklist with theprediction, confidence score, and a recommendation/adjustment to theschedule and/or other workflow element are generated and provided basedon the combination of prediction and confidence score and/or interval. Aresult generated by the inference engine 160 and provided to the dataaccess layer 140 via a REST command can be used to drive dashboard 130output as well as provide output to a scheduler associated with one ormore information systems 110 to adjust equipment, personnel, patient,and/or other resource allocation based on integrated workflowprediction(s) 170, for example. Output can be used to help ensurecompliance with service level agreement(s) (SLA), reduce and/or maintainpatient wait time, and trigger a reminder and/or other preventativeand/or remedial action for one or more patients when the inferenceengine 160 indicates a high likelihood of patient no-show. Such action,triggered by the engine output 130, 170, etc., can improve resourceutilization, patient care, and system responsiveness, for example.

FIGS. 9-14 depict example interfaces generated by the example systemsand methods of FIGS. 1-8. For example, FIG. 9 shows an examplepredictive no-show interface 900. The example interface 900 illustratespredictive patient no-show for radiology examinations based on machinelearning from the inference engine 160, for example. For each scheduledpatient, a tile 910-912 representing the patient and their appointmentis shown in conjunction with weather forecast information 920-922 fortheir appointment. Alternatively or in addition to weather forecastinformation, traffic information, etc., can be provided via the exampleinterface 900 in conjunction with the patient 910-912 and prediction930-932 information. A probability of no-show 930-932 is displayed onthe interface 900, and a rescheduling option 940 is presented when thepatient has a high probability of missing the scheduled appointment(e.g., >50%, etc.).

FIG. 10 shows an example dashboard 1000 listing a set of patients andtheir information, scheduled appointment information, predictedprobability of no-show, etc. A user can interact with the exampledashboard 1000 to evaluate a schedule or workflow and patients includedin that schedule/workflow/worklist. In certain examples, a user canselect a patient's no-show probability to view additional informationthat lead to the generation of the corresponding probability. Selectinga patient and/or other person listed in the dashboard 1000 can retrievecontact information for the person and/or another person to contact themin the event of a no-show, in advance of a probable no-show, etc., toprompt the person to attend, to fill the spot with another person, etc.Via the example interface 1000, a daily schedule, weekly schedule,monthly schedule, etc., can be viewed and modified, for example. Incertain examples, a schedule or worklist can be viewed by modalityand/or other criterion via the example dashboard interface 1000. Theexample interface 1000 can provide predicted no-shows for a givenmodality for a next day's appointments for a user, department, etc., forexample.

FIG. 11 shows an example graph 1100 of predicted no-show probabilitiesfor each hour of a day. Thus, for a particular modality (e.g., x-ray,ultrasound, MR, etc.), a probability of patient no-show can vary by hourthroughout the day. The example graph 1100 conveys the probabilities toa user, for example. Using the example graph 1100, a user can viewattendance prediction(s) and plan for demand, strategize to increasedemand, etc. In certain examples, the graph 1100 is connected to thedashboard 1000 to allow a user to view a wait list and/or contactinformation to try and fill empty schedule slots, help ensure a personshows for an appointment, etc.

FIG. 12 illustrates an example retrospective dashboard 1200 providing ahistorical recap or look back at actual no-shows for patientappointments. As shown in the example of FIG. 12, out of a total numberof exams for a given day and/or other time period, a number and/orpercentage of patient no-shows can be calculated and displayed 1210.Exams and/or other appointments can be broken down by location (e.g.,department, facility, etc.), type, and/or other criterion 1220.Additionally, no-shows can be broken down by patient-type, patient age,visit type, etc., to provide a number of events, a no-show percentage,etc. 1230. Elements of the interface 1200 can be selected and/orotherwise interacted with to retrieve patient records, triggercomparative analysis (e.g., between time periods, locations, eventtypes, patients, etc.),

FIG. 13 illustrates an example predictive dashboard 1300 providing aprediction of patient no-shows for a particular location, appointmenttype, etc. For example, the interface 1300 can provide a number and/orpercentage of likely no-shows out of a total number of appointments fora location (e.g., facility, department, etc.), a time period, an examtype, etc. 1310. The example dashboard 1300 can break down exams bylocation (e.g., facility, department, etc.), type, etc. 1320.Additionally, a no-show probability can be calculated and displayed bytime, location, etc. 1330. As shown in the example of FIG. 13, thedetermined patient no-show probability can be categorized intoprobability levels such as low (e.g., 0-35%), medium (e.g., 35-60%), andhigh (e.g., 60%+). Probability levels can be used to drive scheduleadjustment, notification, etc.

FIG. 14 shows an example random forest output 1400 processing attendancedata collected over a period of time (e.g., one year, two years, threeyears, etc.). In the example of FIG. 14, a confusion matrix can begenerated for monitored patient shows and no-shows such as used intraining of the machine learning model for no-show prediction. In theexample of FIG. 14, a random forest trained and deployed at a healthcarefacility is analyzed to identify a number of correctly identified shows,a number of correctly identified no-shows, a number of missed shows, anda number of false positive no-shows to generate predictions at a 90.8%precision with an 82.6% recall from false negatives.

Flowcharts representative of example machine readable instructions forimplementing and/or executing in conjunction with the example systems,algorithms, and interfaces of FIGS. 1-6 and 9-14 are shown in FIGS. 7-8.In these examples, the machine readable instructions comprise a programfor execution by a processor such as the processor 1512 shown in theexample processor platform 1500 discussed below in connection with FIG.15. The program can be embodied in software stored on a tangiblecomputer readable storage medium such as a CD-ROM, a floppy disk, a harddrive, a digital versatile disk (DVD), a BLU-RAY™ disk, or a memoryassociated with the processor 1512, but the entire program and/or partsthereof could alternatively be executed by a device other than theprocessor 1512 and/or embodied in firmware or dedicated hardware.Further, although the example program is described with reference to theflowchart and/or process(es) illustrated in FIGS. 7-8, many othermethods of implementing the examples disclosed and described here canalternatively be used. For example, the order of execution of the blockscan be changed, and/or some of the blocks described can be changed,eliminated, or combined.

As mentioned above, the example process(es) of FIGS. 7-8 can beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a tangible computer readable storagemedium such as a hard disk drive, a flash memory, a read-only memory(ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example process(es) of FIGS. 7-8 can beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended.

The subject matter of this description may be implemented as stand-alonesystem or for execution as an application capable of execution by one ormore computing devices. The application (e.g., webpage, downloadableapplet or other mobile executable) can generate the various displays orgraphic/visual representations described herein as graphic userinterfaces (GUIs) or other visual illustrations, which may be generatedas webpages or the like, in a manner to facilitate interfacing(receiving input/instructions, generating graphic illustrations) withusers via the computing device(s).

Memory and processor as referred to herein can be stand-alone orintegrally constructed as part of various programmable devices,including for example a desktop computer or laptop computer hard-drive,field-programmable gate arrays (FPGAs), application-specific integratedcircuits (ASICs), application-specific standard products (ASSPs),system-on-a-chip systems (SOCs), programmable logic devices (PLDs), etc.or the like or as part of a Computing Device, and any combinationthereof operable to execute the instructions associated withimplementing the method of the subject matter described herein.

Computing device as referenced herein can include: a mobile telephone; acomputer such as a desktop or laptop type; a Personal Digital Assistant(PDA) or mobile phone; a notebook, tablet or other mobile computingdevice; or the like and any combination thereof.

Computer readable storage medium or computer program product asreferenced herein is tangible (and alternatively as non-transitory,defined above) and can include volatile and non-volatile, removable andnon-removable media for storage of electronic-formatted information suchas computer readable program instructions or modules of instructions,data, etc. that may be stand-alone or as part of a computing device.Examples of computer readable storage medium or computer programproducts can include, but are not limited to, RAM, ROM, EEPROM, Flashmemory, CD-ROM, DVD-ROM or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired electronicformat of information and which can be accessed by the processor or atleast a portion of the computing device.

The terms module and component as referenced herein generally representprogram code or instructions that causes specified tasks when executedon a processor. The program code can be stored in one or more computerreadable mediums.

Network as referenced herein can include, but is not limited to, a widearea network (WAN); a local area network (LAN); the Internet; wired orwireless (e.g., optical, Bluetooth, radio frequency (RF)) network; acloud-based computing infrastructure of computers, routers, servers,gateways, etc.; or any combination thereof associated therewith thatallows the system or portion thereof to communicate with one or morecomputing devices.

The term user and/or the plural form of this term is used to generallyrefer to those persons capable of accessing, using, or benefiting fromthe present disclosure.

FIG. 15 is a block diagram of an example processor platform 1500 capableof executing instructions to implement the example systems and methodsdisclosed and described herein. The processor platform 1500 can be, forexample, a server, a personal computer, a mobile device (e.g., a cellphone, a smart phone, a tablet such as an IPAD™), a personal digitalassistant (PDA), an Internet appliance, or any other type of computingdevice.

The processor platform 1500 of the illustrated example includes aprocessor 1512. The processor 1512 of the illustrated example ishardware. For example, the processor 1512 can be implemented by one ormore integrated circuits, logic circuits, microprocessors or controllersfrom any desired family or manufacturer.

The processor 1512 of the illustrated example includes a local memory1513 (e.g., a cache). The processor 1512 of the illustrated example isin communication with a main memory including a volatile memory 1514 anda non-volatile memory 1516 via a bus 1518. The volatile memory 1514 canbe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory(RDRAM) and/or any other type of random access memory device. Thenon-volatile memory 1516 can be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 1514,1516 is controlled by a memory controller.

The processor platform 1500 of the illustrated example also includes aninterface circuit 1520. The interface circuit 1520 can be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 1522 are connectedto the interface circuit 1520. The input device(s) 1522 permit(s) a userto enter data and commands into the processor 1512. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 1524 are also connected to the interfacecircuit 1520 of the illustrated example. The output devices 1524 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a light emitting diode (LED), a printer and/or speakers).The interface circuit 1520 of the illustrated example, thus, typicallyincludes a graphics driver card, a graphics driver chip or a graphicsdriver processor.

The interface circuit 1520 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network1526 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 1500 of the illustrated example also includes oneor more mass storage devices 1528 for storing software and/or data.Examples of such mass storage devices 1528 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

The coded instructions 1532 can be stored in the mass storage device1528, in the volatile memory 1514, in the non-volatile memory 1516,and/or on a removable tangible computer readable storage medium such asa CD or DVD. The instructions 1532 can be executed by the processor 1512to implement the example system 100, 400, etc., as disclosed anddescribed above.

From the foregoing, it will be appreciated that example methods,apparatus and articles of manufacture have been disclosed that improveprocessing of data and associated documents. The disclosed methods,apparatus and articles of manufacture improve the efficiency of using acomputing device and an interface being driven by the computing deviceby providing relevant documents in the context of a particular patientand exam order for display and interaction via a single interface. Incertain examples, access to the larger set of documents is alsomaintained. Certain examples improve a computer system and its processand user interface display through the ability to apply filters in amanner previously unavailable. While prior approaches did not providesuch matching and filtering and suffered from lack of granularity whichresults in loss of relevant data, computing performance issues, impacton patient safety, etc., certain examples alter the operation of thecomputing device and provide a new interface and document interaction.The disclosed methods, apparatus and articles of manufacture areaccordingly directed to one or more improvement(s) in the functioning ofa computer, as well as a new matching methodology and user interfacelayout, structure, and interaction for patient and exam information.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. A predictive workflow analytics apparatuscomprising: a memory including instructions; and at least one processorto execute the instructions to at least: orchestrate a combination ofnon-healthcare information with healthcare workflow data includingpatient information and a scheduled appointment for the patient;generate a prediction including a probability of a patient no-show tothe scheduled appointment using an artificial intelligence engine in aninferencing mode, the artificial intelligence engine including a patientno-show model to predict the probability of the patient no-show based onthe combination of healthcare workflow data and non-healthcareinformation; synchronize the prediction and a schedule including thescheduled appointment for the patient; and generate an interactivedashboard including the synchronized prediction and the schedule and atleast one of: a) a first option to adjust the schedule to replace thepatient based on the probability of the patient no-show, b) a secondoption to adjust the schedule to move the patient to a different timebased on the probability of the patient no-show.
 2. The apparatus ofclaim 1, wherein the non-healthcare data includes at least one ofweather forecast data, traffic forecast data, or holiday information. 3.The apparatus of claim 1, wherein the patient no-show model is to beimplemented using at least one of a random forest model or a neuralnetwork model.
 4. The apparatus of claim 1, wherein the at least oneprocessor is to operate the artificial intelligence engine in a trainingmode to train the patient no-show model.
 5. The apparatus of claim 4,wherein patient no-show prediction accuracy feedback provided in theinferencing mode is to trigger the at least one processor to enter thetraining mode.
 6. The apparatus of claim 1, wherein the at least oneprocessor is to synchronize the prediction and the schedule using a datacube.
 7. The apparatus of claim 1, wherein the interactive dashboard isto include a third option to send a reminder of the scheduledappointment to the patient.
 8. The apparatus of claim 1, wherein the atleast one processor is to retrieve the healthcare workflow data using afast healthcare interoperability resources application programminginterface that defines the resources as a set of at least one ofoperations or interactions to retrieve the healthcare workflow databased on type.
 9. The apparatus of claim 8, wherein the at least oneprocessor is to interface with a scheduling system to provide theschedule, the prediction, and a selected one of the first option or thesecond option to the scheduling system via the fast healthcareinteroperability resources application programming interface.
 10. Theapparatus of claim 1, wherein the interactive dashboard includes apredictive interface view and a retrospective interface view.
 11. Atleast one computer-readable storage medium comprising instructionswhich, when executed by at least one processor, cause the at least oneprocessor to at least: orchestrate a combination of non-healthcareinformation with healthcare workflow data including patient informationand a scheduled appointment for the patient; generate a predictionincluding a probability of a patient no-show to the scheduledappointment using an artificial intelligence engine in an inferencingmode, the artificial intelligence engine including a patient no-showmodel to predict the probability of the patient no-show based on thecombination of healthcare workflow data and non-healthcare information;synchronize the prediction and a schedule including the scheduledappointment for the patient; and generate an interactive dashboardincluding the synchronized prediction and the schedule and at least oneof: a) a first option to adjust the schedule to replace the patientbased on the probability of the patient no-show, b) a second option toadjust the schedule to move the patient to a different time based on theprobability of the patient no-show.
 12. The at least onecomputer-readable storage medium of claim 11, wherein the non-healthcaredata includes at least one of weather forecast data, traffic forecastdata, or holiday information.
 13. The at least one computer-readablestorage medium of claim 11, wherein the instructions, when executed,cause the at least one processor to operate the artificial intelligenceengine in a training mode to train the patient no-show model.
 14. The atleast one computer-readable storage medium of claim 13, wherein patientno-show prediction accuracy feedback provided in the inferencing mode isto trigger the at least one processor to enter the training mode. 15.The at least one computer-readable storage medium of claim 11, whereinthe interactive dashboard is to include a third option to send areminder of the scheduled appointment to the patient.
 16. The at leastone computer-readable storage medium of claim 11, wherein the at leastone processor is to retrieve the healthcare workflow data using a fasthealthcare interoperability resources application programming interfacethat defines the resources as a set of at least one of operations orinteractions to retrieve the healthcare workflow data based on type. 17.The at least one computer-readable storage medium of claim 16, whereinthe instructions, when executed, cause the at least one processor tointerface with a scheduling system to provide the schedule, theprediction, and a selected one of the first option or the second optionto the scheduling system via the fast healthcare interoperabilityresources application programming interface.
 18. A method to applypredictive analytics to drive a patient care pathway, the methodcomprising: orchestrating a combination of non-healthcare informationwith healthcare workflow data including patient information and ascheduled appointment for the patient; generating a prediction includinga probability of a patient no-show to the scheduled appointment using anartificial intelligence engine in an inferencing mode, the artificialintelligence engine including a patient no-show model to predict theprobability of the patient no-show based on the combination ofhealthcare workflow data and non-healthcare information; synchronizingthe prediction and a schedule including the scheduled appointment forthe patient; and generating an interactive dashboard including thesynchronized prediction and the schedule and at least one of: a) a firstoption to adjust the schedule to replace the patient based on theprobability of the patient no-show, b) a second option to adjust theschedule to move the patient to a different time based on theprobability of the patient no-show.
 19. The method of claim 18, whereinthe non-healthcare data includes at least one of weather forecast data,traffic forecast data, or holiday information.
 20. The method of claim18, further including, triggering, based on patient no-show predictionaccuracy feedback, a training mode to retrain the patient no-show model.